In this course we take you through a focused establishment of R, there are many courses available that you can take to further improve your skills with R programming but the focus here is to enable you to use this language comfortably for your health data science projects. We will cover:
Provide the reader with a succinct summary of your work
Provide an introduction to you portfolio to reader.
covering data access requirements, ethics, metadata and all methodological aspects of your project
Use this section to showcase the results of your data manipulation that will contribute to the project
Summaries your findings,discuss them in the context of other similar work or questions and suggestions for future work. Conclude your portfolio with what started your data exploration and what have the data contributed in the decisions for patient care or health service delivery.
## `geom_smooth()` using formula = 'y ~ x'
## Setting the `off` event (i.e., 'plotly_doubleclick') to match the `on` event (i.e., 'plotly_hover'). You can change this default via the `highlight()` function.
## line plot
Sometimes even with interactive graphs, the picture is not complete. Adding movements to a complex graph can help make the relationship between the variables more transparent and add more meaning to the graphs. To achieve this, we use animations.
A template bubble graph using the airquality dataset in R.
MM.HDS <- data.frame(
Courses = c("Course 1","Course 2","Course 3","Course 4","Course 5"),
Unit_Titles=c(
"Harnessing data for healthcare advancement",
"The impact of big data on healthcare",
"Mastering critical analysis in evidence-based healthcare",
"Navigating complex health data challenges",
"Capstone Assignment"
),
Indicative_Learning_hours = c(
"30 hrs",
"30 hrs",
"30 hrs",
"30 hrs",
"30 hrs"
))
kable(MM.HDS)| Courses | Unit_Titles | Indicative_Learning_hours |
|---|---|---|
| Course 1 | Harnessing data for healthcare advancement | 30 hrs |
| Course 2 | The impact of big data on healthcare | 30 hrs |
| Course 3 | Mastering critical analysis in evidence-based healthcare | 30 hrs |
| Course 4 | Navigating complex health data challenges | 30 hrs |
| Course 5 | Capstone Assignment | 30 hrs |